Knowledge Probing

Knowledge probing is a research area focused on evaluating the factual and conceptual knowledge encoded within large language models (LLMs) and other machine learning systems, particularly assessing their ability to recall and reason with this information. Current research emphasizes developing robust and unbiased probing methods, often using diverse benchmark datasets and focusing on different knowledge types (factual, relational, conceptual) and model architectures (including graph neural networks). These efforts aim to improve the understanding of LLM capabilities, identify limitations such as factual hallucination and sycophantic behavior, and ultimately guide the development of more reliable and accurate AI systems for various applications.